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An evolutionary account of how basketball players grow old

The last time I played basketball was around my friend Peter’s house. We were maybe 15 years old and became distracted by pigeons having sex on the garage roof. I am the kind of human who associates dunking with Rich Tea biscuits and dribbling with sleep. With these caveats, I will talk to you now about basketball.
(To read a beautiful account of amateur basketball, check out John McPhee’s A Sense of Where You Are in the New Yorker. If you’d prefer a hackneyed bodge of professional basketball and some cheap puns, read on.)

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We grow old; we shall wear the bottoms of our trousers rolled. Our bodies slow down and later, we die.

How you age – your pattern of ageing – depends on many things, not least your diet, your social environment and your history (or should I say histories?) of sexy times. Understanding the patterns of ageing is important for a greying world in which more people live longer and invariably need looking after as bodies and minds fail.

So what can basketball tell us about ageing? Professional athletes spend a long time in peak physical condition. They are, in a sense, model organisms (to use the parlance of biology) because they keep a more or less constant body mass and fat/muscle composition over their careers, until retirement.

Basketball is fast and relentless. After receiving the ball, you have just 24 seconds to make a shot (without this rule, basketball apparently struggled to fetch an audience in its early days). All players must score for their team to stand any chance of winning. It all (according to the researchers) boils down to scoring and the ability to score may decline with age. The researchers suggest some important abilities may be prone to ageing: burst speed, strength, motor skills and visual acuity.

They looked at how many different types of shot each player scored per minute of game-time, for every year they played professional basketball. Each shot type requires different abilities. You score a two-pointer by snaking through opponents who are bashing you with elbows and knees and bits. Three pointers – thrown from far away – might need more power, accuracy and maybe better eyesight. And certainly some proverbial balls and the tolerance to deal with shame and booing when you miss. Free throws require calm and consistency.

The researchers also looked at whether there are any differences in performance decline between males in the NBA and females in the WNBA. From the point of view of evolution, we would expect sex differences in how performance declines because, on average, females and males have different body compositions and experience different costs of reproducing: eggs are expensive and sperm is cheap.

Men mass-produce sperm over their lifetimes, making 1,500 sperm cells every second – it’s of no great important if a few million die unejaculated in the testes or get tossed into a bin (as it were). Egg cells, on the other hand, are a scare commodity. Women are born with a limited supply. Also – and this is a biggie – women bear the costs of bearing a child: housing and feeding the foetus in her womb as well as breastfeeding after birth.

So perhaps we would also expect different trajectories of bodily ageing as well.

Of 1,035 NBA players (playing between 1979 and 2010), the researchers found that male performance peaks at around 25 years old then declines. They found no peak across 540 WNBA players (playing between 1998 and 2009). The graph below shows the trends for males (blue, on the left) and females (red, on the right).

Superstars are weird. I don’t just mean that in a Miley Cyrus or Kanye West sense. Superstars, by their very definition, possess talents and skills above that of the average human. They are X-treme, in a statistical sense. Dr Lailvaux’s study deals with average trends over time—the exceptional career trajectory of a Michael Jordan or a Shaquille O’Neal (that’s Dr O’Neal to you) does not render their results meaningless. The big cheeses and the small fry, all are swept up by the confidence intervals (the blue and red flares around the mean trend lines in the graph above).

The blue and red confidence intervals fan out at the beginning and end of the curves because there are relatively few younger or older players. The more examples there are of players at each age, the more confident we can be in the result, so the narrower the confidence interval, as in the middle of the graphs.

I wonder whether the sex differences could partly be down to the relatively more gruelling, persistent nature of the NBA – 82 games excluding playoffs per season compared to WNBA’s 34 games. An NBA season lasts around nine months but the WNBA is around six. Also, in both leagues, those playing for the best teams, the ones who go on to the Finals, are going to play more matches in a year than the worst players.

And what do players do when not playing? To get a fully nuanced view of human ageing trends, one should not consider this dataset or this sport in isolation. These players are humans with lives outside of their careers. Still, the data point to interesting patterns. Perhaps, though, we cannot easily extrapolate these results to ‘normal’ humans and our ageing patterns. How might we decline?